Dummy-coded maps for stimulus modulated expectation ratings

clear all;

close all;

mount_dir = '/Volumes/spacetop_projects_cue/analysis/fmri/spm/univariate/model02_CESeO/1stLevel';

con_list = dir(fullfile(mount_dir, '*/con_0019.nii'));

spm('Defaults','fMRI')

con_fldr = {con_list.folder}; fname = {con_list.name};

con_files = strcat(con_fldr,'/', fname)';

con_data_obj = fmri_data(con_files);

Using default mask: /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/brainmask_canlab.nii Direct calls to spm_defauts are deprecated. Please use spm('Defaults',modality) or spm_get_defaults instead.

sampleto = '/Volumes/spacetop_projects_cue/analysis/fmri/spm/univariate/model02_CESeO/1stLevel/sub-0014/con_0019.nii'

loading mask. mapping volumes. checking that dimensions and voxel sizes of volumes are the same. Pre-allocating data array. Needed: 29951100 bytes Loading image number: 75 Elapsed time is 9.890122 seconds. Image names entered, but fullpath attribute is empty. Getting path info. Number of unique values in dataset: 7217384 Bit rate: 22.78 bits

contrast_name = {'cue_P', 'cue_V', 'cue_C', 'cue_G',...

'stim_P', 'stim_V', 'stim_C', 'stim_G',...

'stimXexpect_P', 'stimXexpect_V', 'stimXexpect_C',...

'motor', ...

'simple_cue_P', 'simple_cue_V', 'simple_cue_C', ...

'simple_stim_P', 'simple_stim_V', 'simple_stim_C',...

'simple_stimXexpect_P', 'simple_stimXexpect_V', 'simple_stimXexpect_C'};

m = mean(con_data_obj);

m.dat = sum(~isnan(con_data_obj.dat) & con_data_obj.dat ~= 0, 2);

orthviews(m, 'trans'); % display

SPM12: spm_check_registration (v7759) 18:56:36 - 12/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

Grouping contiguous voxels: 1 regions

drawnow; snapnow

[wh_outlier_uncorr, wh_outlier_corr] = plot(con_data_obj);

______________________________________________________________ Outlier analysis ______________________________________________________________ global mean | global mean to var | spatial MAD | Missing values | 0 images Retained 4 components for mahalanobis distance Expected 50% of points within 50% normal ellipsoid, found 13.33% Expected 3.75 outside 95% ellipsoid, found 5 Potential outliers based on mahalanobis distance: Bonferroni corrected: 3 images Cases 36 37 48 Uncorrected: 5 images Cases 20 36 37 48 70 Retained 10 components for mahalanobis distance Expected 50% of points within 50% normal ellipsoid, found 58.67% Expected 3.75 outside 95% ellipsoid, found 0 Potential outliers based on mahalanobis distance: Bonferroni corrected: 0 images Cases Uncorrected: 0 images Cases Mahalanobis (cov and corr, q<0.05 corrected): 3 images Outlier_count Percentage _____________ __________ global_mean 3 4 global_mean_to_variance 0 0 missing_values 0 0 rmssd_dvars 0 0 spatial_variability 3 4 mahal_cov_uncor 5 6.6667 mahal_cov_corrected 3 4 mahal_corr_uncor 0 0 mahal_corr_corrected 0 0 Overall_uncorrected 5 6.6667 Overall_corrected 3 4

SPM12: spm_check_registration (v7759) 18:57:07 - 12/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1 (all) /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1 /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

Grouping contiguous voxels: 1 regions

set(gcf,'Visible','on');

figure ('Visible', 'on');

drawnow, snapnow;

con = con_data_obj;

disp(strcat("current length is ", num2str(size(con_data_obj.dat,2))));

current length is 75

%for s = 1:length(wh_outlier_corr)

%disp(strcat("-------subject", num2str(s), "------"))

con.dat = con_data_obj.dat(:,~wh_outlier_corr);

con.image_names = con_data_obj.image_names(~wh_outlier_corr,:);

con.fullpath = con_data_obj.fullpath(~wh_outlier_corr,:);

con.files_exist = con_data_obj.files_exist(~wh_outlier_corr,:);

%end

disp(strcat("after removing ", num2str(sum(wh_outlier_corr)), " participants, size is now ",num2str(size(con.dat,2))));

after removing 3 participants, size is now 72

[path,n,e] = fileparts(con_fldr(wh_outlier_corr));

disp(strcat("participants that are outliers:... ", n));

"participants that are outliers:... sub-0073" "participants that are outliers:... sub-0074" "participants that are outliers:... sub-0091"

disp(n);

{'sub-0073'} {'sub-0074'} {'sub-0091'}

imgs2 = con.rescale('l2norm_images');

t = ttest(imgs2);

One-sample t-test Calculating t-statistics and p-values

orthviews(t);

SPM12: spm_check_registration (v7759) 18:57:13 - 12/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

drawnow, snapnow;

fdr_t = threshold(t, .05, 'fdr');

Image 1 FDR q < 0.050 threshold is 0.004471 Image 1 116 contig. clusters, sizes 1 to 2550 Positive effect: 593 voxels, min p-value: 0.00000072 Negative effect: 8337 voxels, min p-value: 0.00000000

orthviews(fdr_t);

SPM12: spm_check_registration (v7759) 18:57:15 - 12/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

drawnow, snapnow;

create_figure('montage'); axis off;

montage(fdr_t);

Setting up fmridisplay objects

sagittal montage: 142 voxels displayed, 8788 not displayed on these slices

sagittal montage: 209 voxels displayed, 8721 not displayed on these slices

sagittal montage: 147 voxels displayed, 8783 not displayed on these slices

axial montage: 1647 voxels displayed, 7283 not displayed on these slices

axial montage: 1867 voxels displayed, 7063 not displayed on these slices

drawnow, snapnow;

[image_by_feature_correlations, top_feature_tables] = neurosynth_feature_labels( mean(imgs2), 'images_are_replicates', false, 'noverbose');

Input image 1 fullpath_was_empty _____________________________________________________________________ testr_low words_low testr_high words_high _________ _______________ __________ ______________ -0.20503 {'counting' } 0.18719 {'images' } -0.20061 {'calculation'} 0.17035 {'animal' } -0.19181 {'solving' } 0.15629 {'age' } -0.18351 {'numerical' } 0.15384 {'male' } -0.18047 {'1back' } 0.15118 {'trait' } -0.17145 {'arithmetic' } 0.14591 {'depression'} -0.16381 {'numbers' } 0.13763 {'loss' } -0.16346 {'load' } 0.13203 {'expression'} -0.16098 {'rule' } 0.12554 {'approach' } -0.1579 {'card' } 0.12406 {'personal' }

% [image_by_feature_correlations, top_feature_tables] = neurosynth_feature_labels( m, 'images_are_replicates', false, 'noverbose');

[obj, names] = load_image_set('pain_cog_emo');

Loaded images: /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Negative_Emotion.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Negative_Emotion.nii

bpls_wholebrain = get_wh_image(obj, [8 16 24]);

names_wholebrain = names([8 16 24]);

create_figure('Kragel Pain-Cog-Emo maps', 1, 3);

stats = image_similarity_plot(imgs2, 'average', 'mapset', bpls_wholebrain, 'networknames', names_wholebrain, 'nofigure');

Table of correlations Group:1 -------------------------------------- T-test on Fisher's r to Z transformed point-biserial correlations R_avg T P sig Pain Wholebrain -0.0101 -1.3935 0.1678 0.0000 Cog Wholebrain 0.0154 3.5480 0.0007 1.0000 Emo Wholebrain -0.0042 -0.6468 0.5198 0.0000

axis image

subplot(1, 3, 2)

barplot_columns(stats.r', 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)

Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain --------------------------------------------- Tests of column means against zero --------------------------------------------- Name Mean_Value Std_Error T P Cohens_d ___________________ __________ _________ ________ __________ _________ {'Pain Wholebrain'} -0.010058 0.0072395 -1.3893 0.16907 -0.16374 {'Cog Wholebrain' } 0.015373 0.004332 3.5487 0.00069114 0.41822 {'Emo Wholebrain' } -0.004222 0.0064807 -0.65148 0.51684 -0.076777

ans = struct with fields:

fig_han: [1×1 struct] axis_han: [1×1 Axes] bar_han1: [1×1 Bar] bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]} errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]} point_han1: {72×3 cell} text_han: {72×3 cell} point_han: {72×3 cell} star_handles: [9.0013 10.0013 11.0013]

set(gca, 'FontSize', 14)

ylabel('Pattern similarity (r)');

title('Similarity (r) with patterns')

test_data_obj = resample_space(imgs2, bpls_wholebrain);

clear csim

for i = 1:3

csim(:, i) = canlab_pattern_similarity(test_data_obj.dat, bpls_wholebrain.dat(:, i), 'cosine_similarity');

end

Warning: Some images have zero values in some of the 415356 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 Warning: Some images have zero values in some of the 415356 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 Warning: Some images have zero values in some of the 415356 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297

subplot(1, 3, 3)

barplot_columns(csim, 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)

Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain --------------------------------------------- Tests of column means against zero --------------------------------------------- Name Mean_Value Std_Error T P Cohens_d ___________________ __________ _________ ________ _________ _________ {'Pain Wholebrain'} -0.0097923 0.0072831 -1.3445 0.18306 -0.15845 {'Cog Wholebrain' } 0.014306 0.0041202 3.4722 0.0008828 0.40921 {'Emo Wholebrain' } -0.00378 0.0065278 -0.57907 0.56437 -0.068244

ans = struct with fields:

fig_han: [1×1 struct] axis_han: [1×1 Axes] bar_han1: [1×1 Bar] bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]} errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]} point_han1: {72×3 cell} text_han: {72×3 cell} point_han: {72×3 cell} star_handles: [12.0013 13.0013 14.0013]

set(gca, 'FontSize', 14)

ylabel('Pattern similarity (cosine sim)');

title('Pattern response (cosine similarity)')

clear all;

close all;

mount_dir = '/Volumes/spacetop_projects_cue/analysis/fmri/spm/univariate/model02_CESeO/1stLevel';

con_list = dir(fullfile(mount_dir, '*/con_0020.nii'));

spm('Defaults','fMRI');

con_fldr = {con_list.folder}; fname = {con_list.name};

con_files = strcat(con_fldr,'/', fname)';

con_data_obj = fmri_data(con_files);

Using default mask: /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/brainmask_canlab.nii Direct calls to spm_defauts are deprecated. Please use spm('Defaults',modality) or spm_get_defaults instead.

sampleto = '/Volumes/spacetop_projects_cue/analysis/fmri/spm/univariate/model02_CESeO/1stLevel/sub-0014/con_0020.nii'

loading mask. mapping volumes. checking that dimensions and voxel sizes of volumes are the same. Pre-allocating data array. Needed: 29951100 bytes Loading image number: 75 Elapsed time is 11.683513 seconds. Image names entered, but fullpath attribute is empty. Getting path info. Number of unique values in dataset: 7198853 Bit rate: 22.78 bits

contrast_name = {'cue_P', 'cue_V', 'cue_C', 'cue_G',...

'stim_P', 'stim_V', 'stim_C', 'stim_G',...

'stimXexpect_P', 'stimXexpect_V', 'stimXexpect_C',...

'motor', ...

'simple_cue_P', 'simple_cue_V', 'simple_cue_C', ...

'simple_stim_P', 'simple_stim_V', 'simple_stim_C',...

'simple_stimXexpect_P', 'simple_stimXexpect_V', 'simple_stimXexpect_C'};

m = mean(con_data_obj);

m.dat = sum(~isnan(con_data_obj.dat) & con_data_obj.dat ~= 0, 2);

orthviews(m, 'trans') % display

SPM12: spm_check_registration (v7759) 18:50:18 - 12/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

Grouping contiguous voxels: 1 regions

ans = 1×1 cell array

{1×1 region}

drawnow; snapnow;

[wh_outlier_uncorr, wh_outlier_corr] = plot(con_data_obj);

______________________________________________________________ Outlier analysis ______________________________________________________________ global mean | global mean to var | spatial MAD | Missing values | 0 images Retained 5 components for mahalanobis distance Expected 50% of points within 50% normal ellipsoid, found 16.00% Expected 3.75 outside 95% ellipsoid, found 6 Potential outliers based on mahalanobis distance: Bonferroni corrected: 4 images Cases 36 37 43 44 Uncorrected: 6 images Cases 36 37 43 44 48 52 Retained 7 components for mahalanobis distance Expected 50% of points within 50% normal ellipsoid, found 54.67% Expected 3.75 outside 95% ellipsoid, found 0 Potential outliers based on mahalanobis distance: Bonferroni corrected: 0 images Cases Uncorrected: 0 images Cases Mahalanobis (cov and corr, q<0.05 corrected): 4 images Outlier_count Percentage _____________ __________ global_mean 3 4 global_mean_to_variance 0 0 missing_values 0 0 rmssd_dvars 0 0 spatial_variability 3 4 mahal_cov_uncor 6 8 mahal_cov_corrected 4 5.3333 mahal_corr_uncor 0 0 mahal_corr_corrected 0 0 Overall_uncorrected 6 8 Overall_corrected 4 5.3333

SPM12: spm_check_registration (v7759) 18:50:53 - 12/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1 (all) /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1 /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

Grouping contiguous voxels: 1 regions

set(gcf,'Visible','on');

figure ('Visible', 'on');

drawnow, snapnow;

con = con_data_obj;

disp(strcat("current length is ", num2str(size(con_data_obj.dat,2))));

current length is 75

%for s = 1:length(wh_outlier_corr)

% disp(strcat("-------subject", num2str(s), "------"))

con.dat = con_data_obj.dat(:,~wh_outlier_corr);

con.image_names = con_data_obj.image_names(~wh_outlier_corr,:);

con.fullpath = con_data_obj.fullpath(~wh_outlier_corr,:);

con.files_exist = con_data_obj.files_exist(~wh_outlier_corr,:);

%end

disp(strcat("after removing ", num2str(sum(wh_outlier_corr)), " participants, size is now ",num2str(size(con.dat,2))));

after removing 4 participants, size is now 71

[path,n,e] = fileparts(con_fldr(wh_outlier_corr));

disp(strcat("participants that are outliers:... ", n));

"participants that are outliers:... sub-0073" "participants that are outliers:... sub-0074" "participants that are outliers:... sub-0082" "participants that are outliers:... sub-0085"

disp(n);

{'sub-0073'} {'sub-0074'} {'sub-0082'} {'sub-0085'}

imgs2 = con.rescale('l2norm_images');

t = ttest(imgs2);

One-sample t-test Calculating t-statistics and p-values

orthviews(t);

SPM12: spm_check_registration (v7759) 18:50:58 - 12/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

drawnow, snapnow;

fdr_t = threshold(t, .05, 'fdr');

Image 1 FDR q < 0.050 threshold is 0.001308 Image 1 92 contig. clusters, sizes 1 to 495 Positive effect: 76 voxels, min p-value: 0.00000000 Negative effect: 2536 voxels, min p-value: 0.00000000

orthviews(fdr_t);

SPM12: spm_check_registration (v7759) 18:50:59 - 12/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

drawnow, snapnow;

create_figure('montage'); axis off;

montage(fdr_t);

Setting up fmridisplay objects

sagittal montage: 32 voxels displayed, 2580 not displayed on these slices

sagittal montage: 14 voxels displayed, 2598 not displayed on these slices

sagittal montage: 19 voxels displayed, 2593 not displayed on these slices

axial montage: 472 voxels displayed, 2140 not displayed on these slices

axial montage: 502 voxels displayed, 2110 not displayed on these slices

drawnow, snapnow;

[image_by_feature_correlations, top_feature_tables] = neurosynth_feature_labels( mean(imgs2), 'images_are_replicates', false, 'noverbose');

Input image 1 fullpath_was_empty _____________________________________________________________________ testr_low words_low testr_high words_high _________ _______________ __________ _______________ -0.15986 {'retrieval' } 0.11869 {'body' } -0.15056 {'memory' } 0.10287 {'stimulation'} -0.13398 {'solving' } 0.093141 {'frequency' } -0.13282 {'episodic' } 0.083478 {'acoustic' } -0.12877 {'retrieved' } 0.08188 {'motion' } -0.12283 {'rules' } 0.078314 {'sequence' } -0.11932 {'engaged' } 0.077051 {'images' } -0.11826 {'calculation'} 0.072011 {'hand' } -0.11756 {'rule' } 0.07158 {'movements' } -0.11669 {'colour' } 0.069724 {'perception' }

[obj, names] = load_image_set('pain_cog_emo');

Loaded images: /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Negative_Emotion.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Negative_Emotion.nii

bpls_wholebrain = get_wh_image(obj, [8 16 24]);

names_wholebrain = names([8 16 24]);

create_figure('Kragel Pain-Cog-Emo maps', 1, 3);

stats = image_similarity_plot(imgs2, 'average', 'mapset', bpls_wholebrain, 'networknames', names_wholebrain, 'nofigure');

Table of correlations Group:1 -------------------------------------- T-test on Fisher's r to Z transformed point-biserial correlations R_avg T P sig Pain Wholebrain 0.0086 1.3143 0.1930 0.0000 Cog Wholebrain -0.0095 -2.4785 0.0156 1.0000 Emo Wholebrain 0.0004 0.0544 0.9567 0.0000

axis image

subplot(1, 3, 2)

barplot_columns(stats.r', 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)

Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain --------------------------------------------- Tests of column means against zero --------------------------------------------- Name Mean_Value Std_Error T P Cohens_d ___________________ __________ _________ ________ ________ _________ {'Pain Wholebrain'} 0.0086133 0.0065631 1.3124 0.19368 0.15575 {'Cog Wholebrain' } -0.0095278 0.0038445 -2.4783 0.015615 -0.29412 {'Emo Wholebrain' } 0.00036493 0.0068382 0.053366 0.95759 0.0063334

ans = struct with fields:

fig_han: [1×1 struct] axis_han: [1×1 Axes] bar_han1: [1×1 Bar] bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]} errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]} point_han1: {71×3 cell} text_han: {71×3 cell} point_han: {71×3 cell} star_handles: [9.0011 10.0011 11.0011]

set(gca, 'FontSize', 14)

ylabel('Pattern similarity (r)');

title('Similarity (r) with patterns')

test_data_obj = resample_space(imgs2, bpls_wholebrain);

clear csim

for i = 1:3

csim(:, i) = canlab_pattern_similarity(test_data_obj.dat, bpls_wholebrain.dat(:, i), 'cosine_similarity');

end

Warning: Some images have zero values in some of the 415356 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 Warning: Some images have zero values in some of the 415356 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 Warning: Some images have zero values in some of the 415356 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297

subplot(1, 3, 3)

barplot_columns(csim, 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)

Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain --------------------------------------------- Tests of column means against zero --------------------------------------------- Name Mean_Value Std_Error T P Cohens_d ___________________ __________ _________ _______ _________ ________ {'Pain Wholebrain'} 0.0077981 0.0063585 1.2264 0.22415 0.14555 {'Cog Wholebrain' } -0.0097396 0.0035968 -2.7079 0.0085035 -0.32137 {'Emo Wholebrain' } 0.0014892 0.006623 0.22486 0.82275 0.026686

ans = struct with fields:

fig_han: [1×1 struct] axis_han: [1×1 Axes] bar_han1: [1×1 Bar] bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]} errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]} point_han1: {71×3 cell} text_han: {71×3 cell} point_han: {71×3 cell} star_handles: [12.0011 13.0011 14.0011]

set(gca, 'FontSize', 14)

ylabel('Pattern similarity (cosine sim)');

title('Pattern response (cosine similarity)')

clear all; close all;

mount_dir = '/Volumes/spacetop_projects_cue/analysis/fmri/spm/univariate/model02_CESeO/1stLevel';

con_list = dir(fullfile(mount_dir, '*/con_0021.nii'));

spm('Defaults','fMRI');

con_fldr = {con_list.folder}; fname = {con_list.name};

con_files = strcat(con_fldr,'/', fname)';

con_data_obj = fmri_data(con_files);

Using default mask: /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/brainmask_canlab.nii Direct calls to spm_defauts are deprecated. Please use spm('Defaults',modality) or spm_get_defaults instead.

sampleto = '/Volumes/spacetop_projects_cue/analysis/fmri/spm/univariate/model02_CESeO/1stLevel/sub-0014/con_0021.nii'

loading mask. mapping volumes. checking that dimensions and voxel sizes of volumes are the same. Pre-allocating data array. Needed: 29951100 bytes Loading image number: 75 Elapsed time is 10.385014 seconds. Image names entered, but fullpath attribute is empty. Getting path info. Number of unique values in dataset: 7213189 Bit rate: 22.78 bits

contrast_name = {'cue_P', 'cue_V', 'cue_C', 'cue_G',...

'stim_P', 'stim_V', 'stim_C', 'stim_G',...

'stimXexpect_P', 'stimXexpect_V', 'stimXexpect_C',...

'motor', ...

'simple_cue_P', 'simple_cue_V', 'simple_cue_C', ...

'simple_stim_P', 'simple_stim_V', 'simple_stim_C',...

'simple_stimXexpect_P', 'simple_stimXexpect_V', 'simple_stimXexpect_C'};

m = mean(con_data_obj);

m.dat = sum(~isnan(con_data_obj.dat) & con_data_obj.dat ~= 0, 2);

orthviews(m, 'trans'); % display

SPM12: spm_check_registration (v7759) 18:53:36 - 12/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

Grouping contiguous voxels: 1 regions

drawnow; snapnow;

[wh_outlier_uncorr, wh_outlier_corr] = plot(con_data_obj);

______________________________________________________________ Outlier analysis ______________________________________________________________ global mean | global mean to var | spatial MAD | Missing values | 0 images Retained 3 components for mahalanobis distance Expected 50% of points within 50% normal ellipsoid, found 20.00% Expected 3.75 outside 95% ellipsoid, found 5 Potential outliers based on mahalanobis distance: Bonferroni corrected: 3 images Cases 3 41 75 Uncorrected: 5 images Cases 3 41 55 70 75 Retained 11 components for mahalanobis distance Expected 50% of points within 50% normal ellipsoid, found 54.67% Expected 3.75 outside 95% ellipsoid, found 1 Potential outliers based on mahalanobis distance: Bonferroni corrected: 0 images Cases Uncorrected: 1 images Cases 26 Mahalanobis (cov and corr, q<0.05 corrected): 3 images Outlier_count Percentage _____________ __________ global_mean 3 4 global_mean_to_variance 1 1.3333 missing_values 0 0 rmssd_dvars 0 0 spatial_variability 2 2.6667 mahal_cov_uncor 5 6.6667 mahal_cov_corrected 3 4 mahal_corr_uncor 1 1.3333 mahal_corr_corrected 0 0 Overall_uncorrected 6 8 Overall_corrected 4 5.3333

SPM12: spm_check_registration (v7759) 18:54:17 - 12/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1 (all) /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1 /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

Grouping contiguous voxels: 1 regions

set(gcf,'Visible','on');

figure ('Visible', 'on');

drawnow, snapnow;

con = con_data_obj;

disp(strcat("current length is ", num2str(size(con_data_obj.dat,2))));

current length is 75

%for s = 1:length(wh_outlier_corr)

% disp(strcat("-------subject", num2str(s), "------"))

con.dat = con_data_obj.dat(:,~wh_outlier_corr);

con.image_names = con_data_obj.image_names(~wh_outlier_corr,:);

con.fullpath = con_data_obj.fullpath(~wh_outlier_corr,:);

con.files_exist = con_data_obj.files_exist(~wh_outlier_corr,:);

%end

disp(strcat("after removing ", num2str(sum(wh_outlier_corr)), " participants, size is now ",num2str(size(con.dat,2))));

after removing 4 participants, size is now 71

[path,n,e] = fileparts(con_fldr(wh_outlier_corr));

disp(strcat("participants that are outliers:... ", n));

"participants that are outliers:... sub-0016" "participants that are outliers:... sub-0079" "participants that are outliers:... sub-0100" "participants that are outliers:... sub-0133"

disp(n);

{'sub-0016'} {'sub-0079'} {'sub-0100'} {'sub-0133'}

imgs2 = con.rescale('l2norm_images');

t = ttest(imgs2);

One-sample t-test Calculating t-statistics and p-values

orthviews(t);

SPM12: spm_check_registration (v7759) 18:54:24 - 12/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

drawnow, snapnow;

fdr_t = threshold(t, .05, 'fdr');

Image 1 FDR q < 0.050 threshold is 0.000335 Image 1 15 contig. clusters, sizes 1 to 271 Positive effect: 113 voxels, min p-value: 0.00000000 Negative effect: 559 voxels, min p-value: 0.00000000

orthviews(fdr_t);

SPM12: spm_check_registration (v7759) 18:54:25 - 12/01/2023 ======================================================================== Display /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/keuken_2014_enhanced_for_underlay.img,1

drawnow, snapnow;

create_figure('montage'); axis off;

montage(fdr_t);

Setting up fmridisplay objects

sagittal montage: 22 voxels displayed, 650 not displayed on these slices

sagittal montage: 1 voxels displayed, 671 not displayed on these slices

sagittal montage: 6 voxels displayed, 666 not displayed on these slices

axial montage: 159 voxels displayed, 513 not displayed on these slices

axial montage: 150 voxels displayed, 522 not displayed on these slices

drawnow, snapnow;

[image_by_feature_correlations, top_feature_tables] = neurosynth_feature_labels( mean(imgs2), 'images_are_replicates', false, 'noverbose');

Input image 1 fullpath_was_empty _____________________________________________________________________ testr_low words_low testr_high words_high _________ ________________ __________ __________________ -0.063503 {'associations'} 0.097361 {'empathy' } -0.061685 {'frequency' } 0.095097 {'counting' } -0.059556 {'visuospatial'} 0.091249 {'gonogo' } -0.057487 {'pair' } 0.089328 {'go' } -0.055975 {'adaptation' } 0.087797 {'feelings' } -0.054101 {'pairs' } 0.083917 {'tom' } -0.049129 {'priming' } 0.082425 {'nogo' } -0.046734 {'recognition' } 0.077818 {'empathic' } -0.046251 {'identity' } 0.076653 {'unpleasantness'} -0.044871 {'english' } 0.075319 {'psychophysical'}

[obj, names] = load_image_set('pain_cog_emo');

Loaded images: /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Pain.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Pain.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Cognitive_Control.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Cognitive_Control.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Negative_Emotion.nii /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Negative_Emotion.nii /Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Negative_Emotion.nii

bpls_wholebrain = get_wh_image(obj, [8 16 24]);

names_wholebrain = names([8 16 24]);

create_figure('Kragel Pain-Cog-Emo maps', 1, 3);

stats = image_similarity_plot(imgs2, 'average', 'mapset', bpls_wholebrain, 'networknames', names_wholebrain, 'nofigure');

Table of correlations Group:1 -------------------------------------- T-test on Fisher's r to Z transformed point-biserial correlations R_avg T P sig Pain Wholebrain 0.0016 0.2361 0.8140 0.0000 Cog Wholebrain -0.0027 -0.5382 0.5921 0.0000 Emo Wholebrain 0.0009 0.1139 0.9096 0.0000

axis image

subplot(1, 3, 2)

barplot_columns(stats.r', 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)

Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain --------------------------------------------- Tests of column means against zero --------------------------------------------- Name Mean_Value Std_Error T P Cohens_d ___________________ __________ _________ ________ _______ _________ {'Pain Wholebrain'} 0.001583 0.0067793 0.23351 0.81604 0.027713 {'Cog Wholebrain' } -0.0026979 0.0050104 -0.53846 0.59197 -0.063903 {'Emo Wholebrain' } 0.00091326 0.0077085 0.11847 0.90603 0.01406

ans = struct with fields:

fig_han: [1×1 struct] axis_han: [1×1 Axes] bar_han1: [1×1 Bar] bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]} errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]} point_han1: {71×3 cell} text_han: {71×3 cell} point_han: {71×3 cell} star_handles: [9.0012 10.0012 11.0012]

set(gca, 'FontSize', 14)

ylabel('Pattern similarity (r)');

title('Similarity (r) with patterns')

test_data_obj = resample_space(imgs2, bpls_wholebrain);

clear csim

for i = 1:3

csim(:, i) = canlab_pattern_similarity(test_data_obj.dat, bpls_wholebrain.dat(:, i), 'cosine_similarity');

end

Warning: Some images have zero values in some of the 415356 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 Warning: Some images have zero values in some of the 415356 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 Warning: Some images have zero values in some of the 415356 voxels in weight mask. These will be excluded from similarity analysis image-wise. Number of zero or NaN values within weight mask, by input image: 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297 297

subplot(1, 3, 3)

barplot_columns(csim, 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)

Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain --------------------------------------------- Tests of column means against zero --------------------------------------------- Name Mean_Value Std_Error T P Cohens_d ___________________ __________ _________ ________ _______ _________ {'Pain Wholebrain'} 0.00072591 0.0065704 0.11048 0.91234 0.013112 {'Cog Wholebrain' } -0.0023482 0.0047767 -0.49159 0.62455 -0.058341 {'Emo Wholebrain' } 0.0014649 0.0075649 0.19365 0.84701 0.022982

ans = struct with fields:

fig_han: [1×1 struct] axis_han: [1×1 Axes] bar_han1: [1×1 Bar] bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]} errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]} point_han1: {71×3 cell} text_han: {71×3 cell} point_han: {71×3 cell} star_handles: [12.0012 13.0012 14.0012]

set(gca, 'FontSize', 14)

ylabel('Pattern similarity (cosine sim)');

title('Pattern response (cosine similarity)')

drawnow, snapnow;

% save html

pubdir = pwd;

pubfilename = 's03_PVC_stim_dummy_ROI.mlx';

p = struct('useNewFigure', false, 'maxHeight', 800, 'maxWidth', 800, ...

'format', 'html', 'outputDir', pubdir, ...

'showCode', true, 'stylesheet', which('mxdom2simplehtml_CANlab.xsl'));

htmlfile = publish(pubfilename, p);

Dummy-coded maps for stimulus modulated expectation ratings

Pain modulated w/ expectation ratings (dummy coded; painXexpectation > rest)

Pain x Expectation :: load dataset

Pain x Expectation :: check data coverage

Pain x Expectation :: Plot diagnostics, before l2norm

Pain x Expectation :: run robfit

Pain x Expectation :: remove outliers based on plot

Pain x Expectation :: plot diagnostics, after l2norm

Pain x Expectation :: ttest

Pain x Expectation :: Neurosynth similarity

Vicarious modulated w/ expectation ratings (dummy coded; vicariousXexpectation > rest)

Vicarious x Expectation :: load dataset

Vicarious x Expectation :: check data coverage

Vicarious x Expectation :: Plot diagnostics, before l2norm

Vicarious x Expectation :: run robfit

Vicarious x Expectation :: remove outliers based on plot

Vicarious x Expectation :: plot diagnostics, after l2norm

Vicarious x Expectation :: ttest

Vicarious x Expectation :: Neurosynth similarity

Vicarious x Expectation :: Pattern Phil

Cognitive modulated w/ expectation ratings (dummy coded; cognitiveXexpectation > rest)

Cognitive x Expectation :: load dataset

Cognitive x Expectation :: check data coverage

Cognitive x Expectation :: Plot diagnostics, before l2norm

Cognitive x Expectation :: run robfit

Cognitive x Expectation :: remove outliers based on plot

Cognitive x Expectation:: plot diagnostics, after l2norm

Cognitive x Expectation :: ttest

Cognitive x Expectation :: Neurosynth similarity

Cognitive x Expectation :: Pattern Phil